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koxy-ai/
gte-small

Name: gte-small
Rating: 2.5 (14 reviews)

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gte-small

General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning

The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.

Metrics

Performance of the GTE models compared with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the MTEB leaderboard.

Model Name	Model Size (GB)	Dimension	Sequence Length	Average (56)	Clustering (11)	Pair Classification (3)	Reranking (4)	Retrieval (15)	STS (10)	Summarization (1)	Classification (12)
gte-large	0.67	1024	512	63.13	46.84	85.00	59.13	52.22	83.35	31.66	73.33
gte-base	0.22	768	512	62.39	46.2	84.57	58.61	51.14	82.3	31.17	73.01
e5-large-v2	1.34	1024	512	62.25	44.49	86.03	56.61	50.56	82.05	30.19	75.24
e5-base-v2	0.44	768	512	61.5	43.80	85.73	55.91	50.29	81.05	30.28	73.84
gte-small	0.07	384	512	61.36	44.89	83.54	57.7	49.46	82.07	30.42	72.31
text-embedding-ada-002	-	1536	8192	60.99	45.9	84.89	56.32	49.25	80.97	30.8	70.93
e5-small-v2	0.13	384	512	59.93	39.92	84.67	54.32	49.04	80.39	31.16	72.94
sentence-t5-xxl	9.73	768	512	59.51	43.72	85.06	56.42	42.24	82.63	30.08	73.42
all-mpnet-base-v2	0.44	768	514	57.78	43.69	83.04	59.36	43.81	80.28	27.49	65.07
sgpt-bloom-7b1-msmarco	28.27	4096	2048	57.59	38.93	81.9	55.65	48.22	77.74	33.6	66.19
all-MiniLM-L12-v2	0.13	384	512	56.53	41.81	82.41	58.44	42.69	79.8	27.9	63.21
all-MiniLM-L6-v2	0.09	384	512	56.26	42.35	82.37	58.04	41.95	78.9	30.81	63.05
contriever-base-msmarco	0.44	768	512	56.00	41.1	82.54	53.14	41.88	76.51	30.36	66.68
sentence-t5-base	0.22	768	512	55.27	40.21	85.18	53.09	33.63	81.14	31.39	69.81

Usage

Deno

javascript

import { env, pipeline } from "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.0";

// Some config for Deno
env.useBrowserCache = false;
env.allowLocalModels = false;

// Give it any input you want
const input = "Hello AI";

// Create the pipeline
const pipe = await pipeline(
  "feature-extraction",
  "koxy-ai/gte-small"
);

// Generate the embedding
const output = await pipe(input, {
  pooling: "mean",
  normalize: true
});

// Extract the embedding from the output
const embedding = Array.from(output.data);

// Do anything with the embedding
console.log(embedding);

Browser

Using Javascript modules.

javascript

<script type="module">

import { pipeline } from "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.0";

// Create the pipeline
const setPipe = async () => {
  return await pipeline(
    "feature-extraction",
    "koxy-ai/gte-small"
  );
};

const generateEmbedding = async (input) => {
  const pipe = await setPipe();
  const output = await pipe(input, {
    pooling: "mean",
    normalize: true
  });
  return Array.from(output.data);
};

export default generateEmbedding;

</script>

Node JS

npm i @xenova/transformers

javascript

import { pipeline } from "@xenova/transformers";

(async () => {
  // Give it any input you want
  const input = "Hello AI";

  // Create the pipeline
  const pipe = await pipeline(
    "feature-extraction",
    "koxy-ai/gte-small"
  );

  // Generate the embedding
  const output = await pipe(input, {
    pooling: "mean",
    normalize: true
  });

  // Extract the embedding from the output
  const embedding = Array.from(output.data);

  // Do anything with the embedding
  console.log(embedding);
})();

Limitation

This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.

Citation

@misc{li2023general,
      title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, 
      author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
      year={2023},
      eprint={2308.03281},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}